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Interactive image quantification tools in nuclear material forensics
Author(s) -
Reid Porter,
Christy E. Ruggiero,
Don Hush,
Neal R. Harvey,
Patrick J. Kelly,
Wayne Scoggins,
Lav Tandon
Publication year - 2011
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.877319
Subject(s) - computer science , domain (mathematical analysis) , task (project management) , feature extraction , segmentation , process (computing) , domain knowledge , subject matter expert , software , exploit , artificial intelligence , human–computer interaction , computer vision , information retrieval , expert system , systems engineering , mathematical analysis , mathematics , programming language , operating system , computer security , engineering
Morphological and microstructural features visible in microscopy images of nuclear materials can give information about the processing history of a nuclear material. Extraction of these attributes currently requires a subject matter expert in both microscopy and nuclear material production processes, and is a time consuming, and at least partially manual task, often involving multiple software applications. One of the primary goals of computer vision is to find ways to extract and encode domain knowledge associated with imagery so that parts of this process can be automated. In this paper we describe a user-in-the-loop approach to the problem which attempts to both improve the efficiency of domain experts during image quantification as well as capture their domain knowledge over time. This is accomplished through a sophisticated user-monitoring system that accumulates user-computer interactions as users exploit their imagery. We provide a detailed discussion of the interactive feature extraction and segmentation tools we have developed and describe our initial results in exploiting the recorded user-computer interactions to improve user productivity over time.

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